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Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of hos...

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Autores principales: AlQadi, Hadeel, Bani-Yaghoub, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993010/
https://www.ncbi.nlm.nih.gov/pubmed/35395018
http://dx.doi.org/10.1371/journal.pone.0265815
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author AlQadi, Hadeel
Bani-Yaghoub, Majid
author_facet AlQadi, Hadeel
Bani-Yaghoub, Majid
author_sort AlQadi, Hadeel
collection PubMed
description Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of host movements in disease models. To demonstrate improved accuracy, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating the global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for susceptible individuals traveling outside of their community: • They can return to the community without any exposure to the infection. • They can be exposed and develop symptoms after returning to the community. • They can be tested positively during the trip and remain quarantined until fully recovered. To examine the predictive accuracy of the extended SIR model, we studied the prevalence of the COVID-19 infection in six randomly selected cities and states in the United States: Kansas City, Saint Louis, San Francisco, Missouri, Illinois, and Arizona. The extended SIR model was parameterized using a two-step model-fitting algorithm. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracy of disease models can be significantly improved by incorporating the global dynamics of the infection.
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spelling pubmed-89930102022-04-09 Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model AlQadi, Hadeel Bani-Yaghoub, Majid PLoS One Research Article Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of host movements in disease models. To demonstrate improved accuracy, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating the global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for susceptible individuals traveling outside of their community: • They can return to the community without any exposure to the infection. • They can be exposed and develop symptoms after returning to the community. • They can be tested positively during the trip and remain quarantined until fully recovered. To examine the predictive accuracy of the extended SIR model, we studied the prevalence of the COVID-19 infection in six randomly selected cities and states in the United States: Kansas City, Saint Louis, San Francisco, Missouri, Illinois, and Arizona. The extended SIR model was parameterized using a two-step model-fitting algorithm. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracy of disease models can be significantly improved by incorporating the global dynamics of the infection. Public Library of Science 2022-04-08 /pmc/articles/PMC8993010/ /pubmed/35395018 http://dx.doi.org/10.1371/journal.pone.0265815 Text en © 2022 AlQadi, Bani-Yaghoub https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
AlQadi, Hadeel
Bani-Yaghoub, Majid
Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
title Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
title_full Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
title_fullStr Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
title_full_unstemmed Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
title_short Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
title_sort incorporating global dynamics to improve the accuracy of disease models: example of a covid-19 sir model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993010/
https://www.ncbi.nlm.nih.gov/pubmed/35395018
http://dx.doi.org/10.1371/journal.pone.0265815
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